AGRICULTURE g NPL can provide data on how wheat is
distributed in the field, along with the ability to zoom into the 3D point cloud to view individual plants. ‘Te data is more accurate, and they’ve
also got year-on-year comparisons, rather than ruler measurements and pictures which don’t often tell them everything they need to know,’ Dudley said.
Image capture Te minimum area of land the NPL is asked to measure is 3 x 2 metres. Tis is captured at millimetre resolution, volumetrically across about half a metre. ‘It’s quite a big volume and quite a high resolution, and you can’t spend too long because things like wind and movement in the crop get in the way. So you need to capture it relatively quickly,’ explained Dudley. Time-of-flight imaging is reasonably fast,
offering 24fps or more, according to Dudley, but these cameras often don’t have the resolution required. Te Photoneo camera, an area-scan device operating by what the Slovakian firm calls parallel structured light, has higher resolution and can capture movement in 3D – the Phoxi XL camera captures up to 3.2 million 3D points for each scan, at 16 million points per second throughput – although it still doesn’t meet all the demands of NPL. To cover a 3 x 2-metre plot, NPL would use
up to 18 cameras, possibly including three Photoneo scanners. Te point clouds from these cameras are then combined to give an image from different angles. Which imaging technique is used comes down to the area of land covered, the speed of capture, and the resolution required. Tere are difficulties in overlapping the
images and getting the complete view, Dudley said, and errors can be produced when combining point clouds. Te measurements are made early in the day or late in the evening when the
sun is low in the sky, because practically every camera technology used struggles with direct sunlight, according to Dudley. ‘I still think there’s a long way to go on most scanners out there in terms of [operating in] daylight,’ he said. ‘Midday, bright sunny day, most systems still fail.’ He added that lidar is the only technology
that can just about deal with bright sunlight, but that lidar has shortcomings for the NPL team in other ways.
Making sense of the data NPL is developing algorithms that fit to individual ears of wheat. Te algorithms first identify where the ears are, then calculate the volume of each ear along with other aspects like length and number of grains. ‘It’s not trivial; it’s pretty tough stuff,’
Dudley said. ‘Fitting shapes into complex point clouds, although it’s been done a bit for autonomous vehicles, it’s still pretty challenging.’ Te researchers are taking two approaches
to analysing the data. Te first is clustering the 3D points using a combination of MVTec’s Halcon image processing software, also supplied by Multipix Imaging, and Matlab vision algorithms. Tese look for densities of points and try to separate noise and stems, which are generally thinner than the ears. Shapes that resemble an ear of wheat are then fitted to find the ears in the point cloud. Te other approach is based on machine learning. Te algorithm learns what an ear
‘Fitting shapes into complex point clouds, although it’s been done a bit for automomous vehicles, it’s still pretty challenging’
Dr Richard Dudley, science area leader for electromagnetics and precision agriculture at NPL
of wheat looks like from classified ears, then hunts them in the point cloud. But these shapes aren’t individual objects sitting with nothing around them; often the ears are overlapping or touching, which creates strange double-headed shapes. Dudley said that the team is starting to get
up to 90 per cent identification in an image of around 100 to 200 ears of wheat. Te data produced after this pre-processing is then much higher quality. At the moment, the NPL team captures
the data in the field and then post-processes it in the lab. Tat can be done within a day. However, the team is also starting to work with groups that have 5G capability out in the field. It then becomes possible to do some cloud computing on these large datasets.
‘Tis is much more [relating to] commercial farming, where you might want to make decisions on the vehicle that’s out in the field,’ Dudley said. ‘Tat’s the future, trying to do data processing on-the-fly and have feedback as a vehicle is progressing through the field. Te 5G network, with its capability of sending high data rates, is certainly something we’re looking at using.’
Food security Dudley made the point that, currently, global production of wheat is struggling. ‘It’s difficult to produce as much grain from the area of land we’ve got, so it’s really important to develop these new varieties of crop that yield more, so we’ve got food for the future. Wheat is one of the main staples in our diet; it accounts for a lot of calories and protein,’ he continued. ‘Tat’s the driver, both economic and sustainability for the future.’ NPL is trying to secure its first customers
for next season, starting in June. It will be going out to customers and measuring their field plots. Dudley said that he is still looking for new
imaging technologies to capture these 3D plots, both faster and in higher resolution, and with lower sensitivity to sunlight. O
14 IMAGING AND MACHINE VISION EUROPE FEBRUARY/MARCH 2020 @imveurope |
www.imveurope.com
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